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ORIGINAL RESEARCH article

Front. Aging Neurosci.
Sec. Parkinson’s Disease and Aging-related Movement Disorders
Volume 16 - 2024 | doi: 10.3389/fnagi.2024.1457405

Deep-learning-based segmentation of perivascular spaces on T2-Weighted 3T magnetic resonance images

Provisionally accepted
Die Cai Die Cai 1Minmin Pan Minmin Pan 2Chenyuan Liu Chenyuan Liu 3Wenjie He Wenjie He 1Xinting Ge Xinting Ge 2Jiaying Lin Jiaying Lin 4Rui Li Rui Li 1Mengting Liu Mengting Liu 4Jun Xia Jun Xia 1*
  • 1 Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen second people's hospital, Shenzhen, China
  • 2 School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province, China
  • 3 Xiangya School of Medicine, Central South University, Changsha, Hunan Province, China
  • 4 School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China

The final, formatted version of the article will be published soon.

    Purpose: Studying perivascular spaces (PVSs) is important for understanding the pathogenesis and pathological changes of neurological disorders. Although some methods for automated segmentation of PVSs have been proposed, most of them were based on 7T MR images that were majorly acquired in healthy young people. Notably, 7T MR imaging is rarely used in clinical practice. Herein, we propose a deeplearning-based method that enables automatic segmentation of PVSs on T2-weighted 3T MR images. Method: Twenty patients with Parkinson's disease (age range, 42-79 years) participated in this study. Specifically, we introduced a multi-scale supervised dense nested attention network designed to segment the PVSs. This model fosters progressive interactions between high-level and low-level features. Simultaneously, it utilises multi-scale foreground content for deep supervision, aiding in refining segmentation results at various levels. Result: Our method achieved the best segmentation results compared with the four other deep-learning-based methods, achieving a dice similarity coefficient (DSC) of 0.702. The results of the visual count of the PVSs in our model correlated extremely well with the expert scoring results on the T2-weighted images (basal ganglia: rs=0.845, P<0.001; rs=0.868, P<0.001; centrum semiovale: rs=0.845, P<0.001; rs=0.823, P<0.001 for raters 1 and 2, respectively). Experimental results show that the proposed method performs well in the segmentation of PVSs.The proposed method can accurately segment PVSs; it will facilitate practical clinical applications and is expected to replace the method of visual counting directly on T1-weighted images or T2-weighted images.

    Keywords: Perivascular spaces, Virchow-Robin spaces, deep learning, Multiscale supervised, Dense nesting, 3T MR image

    Received: 30 Jun 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 Cai, Pan, Liu, He, Ge, Lin, Li, Liu and Xia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jun Xia, Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen second people's hospital, Shenzhen, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.